Large Language Model-Based Text-to-Cypher Generation for Question-Answering on Biomedical Knowledge Graphs.

Suteera Seeha¹, Adem Abdelmoula¹, Martin Boeker¹, Luise Modersohn¹, Peter Pallaoro^1,2

¹Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital.

²Technical University of Munich, TUM School of Medicine and Health, Data Integration Center, TUM University Hospital.

PubMed DOI

Abstract

INTRODUCTION: Knowledge graphs are widely adopted due to their flexible structure and ability to represent large-scale, heterogeneous data. However, as these graphs grow, the queries used to retrieve information become more complex, making efficient information retrieval and question answering more challenging. OBJECTIVE: This work investigates (1) the impact of few-shot versus zero-shot promptin…

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